NCC Intermediate
Short Course in Data Science
Introduction
The NCC Intermediate Short Course in Data Science advances foundational knowledge by focusing on statistical techniques, data modeling, and Python programming, equipping learners with practical skills for career growth in data science.
Table of content
NCC Intermediate Short Course in Data Science
Course Title and Duration
Course Title
NCC Intermediate Short Course in Data Science
Duration
Varies based on the pace of learning. Typically designed for flexible learning.
Start Dates
Enrolment is available throughout the year.
Awarding Institution and Language of Study
Awarding Institution:
NCC Education
Language of Study:
English
Programme Overview
The NCC Intermediate Short Course in Data Science builds upon the foundational skills acquired in introductory data science courses. This intermediate course provides students with a deeper understanding of data science concepts, statistical techniques, data modeling, and programming. The course is designed to equip learners with practical skills and knowledge required to advance their data science careers or further academic study.
Entry Requirements
- Basic understanding of data science principles.
- Prior completion of an introductory data science course is recommended.
- Competency in English.
Programme Structure
The course consists of the following modules:
- Introduction to Data Science
- What is Data Science?
- Roles and responsibilities of Data Scientists.
- Essential skills for data scientists.
- The Data Science Process.
- Learning Outcomes: Understand the fundamental concepts of data science and the roles of data scientists.
- Probability Theory and Random Variables
- Probability concepts and theorems.
- Understanding random variables and their properties.
- Probability distributions and their types.
- Learning Outcomes: Apply probability theory and understand random variables and their distributions.
- Statistics Concepts
- Importance of statistics in data science.
- Quantitative and qualitative analysis.
- Descriptive and inferential statistics.
- Measures of central tendency and dispersion.
- Learning Outcomes: Use statistical methods for analyzing data and drawing conclusions.
- Linear Regression Analysis and Statistical Data Modelling Techniques
- Different types of regression and their applications.
- Data modeling techniques such as Logistic Regression, Classification, K Nearest Neighbors, SVM, Decision Trees, Random Forests, Neural Networks, and Unsupervised Learning.
- Learning Outcomes: Develop and apply various statistical and machine learning models for data analysis.
- Introduction to Python
- Python programming essentials for data science.
- Fundamental concepts, data types, functions, and data structures.
- Learning Outcomes: Gain proficiency in Python programming relevant to data science tasks.
- Python with SQL
- Integrating SQL with Python for database management.
- Creating databases and tables using SQL.
- Learning Outcomes: Use SQL with Python for data storage, retrieval, and manipulation.
- Visualisation with Matplotlib
- Using Matplotlib for data visualization.
- Different layers and tools for creating visualizations.
- Learning Outcomes: Create and interpret visualizations to understand data better.
- Database System
- Fundamentals of database management systems.
- E-R models, entities, attributes, and relationships.
- Learning Outcomes: Understand database systems and their role in data science.
- Design and Develop Database System using SQL
- Database design principles, normalization, and integrity constraints.
- Using SQL for defining and manipulating data.
- Learning Outcomes: Design and manage databases effectively using SQL.
- Text Mining and Natural Language Processing (NLP)
- Understanding NLP and building NLP pipelines.
- Techniques used in text mining.
- Learning Outcomes: Apply text mining techniques and NLP for data extraction and analysis.
- Text Classification
- Techniques for text classification, including supervised learning and various classifiers.
- Learning Outcomes: Classify text data using different models and methods.
- Text Clustering
- Understanding clustering, distance metrics, and clustering algorithms.
- Applications of text clustering in data science.
- Learning Outcomes: Perform text clustering and evaluate clustering results.
Learning & Teaching Strategies
The program uses a blend of online learning methods, including:
Video Lectures
Pre-recorded lectures covering theoretical aspects of the modules.
Tutorials
Online tutorials and quizzes to reinforce learning.
Practical Exercises
Hands-on activities using data science tools and programming.
Discussion Forums
Collaborative platforms for discussions with peers and instructors.
Live Sessions
Real-time sessions for Q&A and deeper dives into complex topics.
Assessment Strategy
Assessment includes:
Coursework
Assignments and projects that require practical application of the course material.
Quizzes
Regular quizzes to test understanding and retention of concepts.
Final Assessment
A comprehensive assessment to evaluate the overall learning outcomes.
Learning Outcomes:
Upon successful completion of the program, students will be able to:
Knowledge & Understanding:
Demonstrate an intermediate understanding of data science, statistical methods, and data modeling techniques.
Cognitive & Intellectual Skills:
Apply advanced statistical methods and machine learning models to real-world data.
Practical & Professional Skills:
Utilize Python and SQL for data manipulation, analysis, and database management.
Transferable Skills:
Communicate findings effectively, work collaboratively on data projects, and apply critical thinking to solve complex data problems.
Career and Professional Development
Completion of this course will prepare students for roles such as:
- Data Analyst
- Junior Data Scientist
- Database Manager
- Business Intelligence Analyst
Support for Student Learning:
Students will have access to:
Personal Tutors
For guidance and academic support.
Learning Resources
Comprehensive online materials and libraries.
Interactive Platforms
For discussions and collaborative learning.
Total Qualification Time:
600 hours (including 305 Guided Learning Hours)
Conclusion
In conclusion, the NCC Intermediate Short Course in Data Science bridges foundational learning with advanced concepts, equipping students with essential statistical, programming, and data modeling skills. By completing this course, learners are well-prepared to tackle real-world data challenges and advance their careers in roles such as Data Analyst, Junior Data Scientist, or Business Intelligence Analyst, while also paving the way for further academic pursuits in the field.
